Sort the first variable's data in descending order. If, for instance, you were looking at data pertaining to how much water was purchased on different days according to temperature, you would first sort the data according to temperatures. Rank this data in a new column; for example, the hottest temperature would be ranked as 1, the next hottest as 2 and so on.
Sort the data from the second variable in descending order. In the example described above, this would be number of bottles of water sold. Rank the data in a new column, so that the day with the highest number of bottles sold was ranked 1, the second highest 2 and so on.
Write the rankings of the two variables next to each other in different columns. Write the difference between the two rankings in a third column. If the most water was sold on the hottest day, the third column would read zero; if the second largest amount of water was sold on the third hottest day, the third column would read 1.
Square each number in the third column. Multiply each number by itself, and write the answer in a new column.
Add all of the numbers in the fourth column. Multiply this number by six. Write the number.
Multiply the total number of measurements by itself. If you tested the temperature on 10 days, the total would be 100. Subtract one from this number; following the example, the sum would be 99. Multiply this number by the number of measurements. In the example, the total would be 990. Alternatively, you can reach this number by multiplying the number of measurements by itself, multiplying the total by the number of measurements again, and subtracting the number of measurements from this number. Write the number.
Subtract the number obtained in Step 6 from the number obtained in Step 5. Write this number.
Subtract the number obtained in Step 7 from one. This number is the Spearman's Rank Correlation Coefficient for your data.